Someone actually said to me, “But it’s not using Deep Learning, so how is it AI?”

For context, the goal of our new Evid Science platform is to read the medical literature. Just like a person, we aim to take written words and convert them into some meaningful, internal representation. As people, this representation is knowledge. For the Evid Science AI, the representation is a clean, standardized representation of the results published in the paper. In some ways, you can’t really get more artificially intelligent than trying to replicate reading.

But I’m not berating anyone. In fact, I sympathize with the first statement in this article. These topics are confusing. Unless you are deeply embedded within AI (I’m close to a decade in the field myself), it’s easy to see how reading all of the amazing things in the news can make it unclear. Quite non-scientifically, I tried to quantify this. I looked at Google News articles that mentioned “Deep Learning,” since 1/1/2016. There were about 55,000. Then, I searched for articles that mentioned both “Deep Learning” and “AI” (or “Artificial Intelligence”). There were 34,000 of those. So, more than 60% of the time, if you read an article about Deep Learning, it will mention Artificial Intelligence! But, crucially, the articles don’t often make the clearest distinctions between the two. So I can see how there might be some conflating between the terms.

So, with that in mind, I hope to clear up some of the confusion in terminology between Artificial Intelligence, Machine Learning and Deep Learning. And I’ll aim specifically for the healthcare audience in my examples.

What is Artificial Intelligence?

Well, honestly, this is a hard one; there isn’t really a crisp definition for Intelligence in the first place, let alone what it means to do it “artificially” (Google: “Dennett Chalmers debate” for some interesting thoughts on what it even means to be conscious). But, for our purposes, it’s having a machine do a cognitive task. That is, it’s not AI for a robot to drill a hole. It is AI if the robot has to find the best place to drill a hole. If you have to think about what you are doing, that’s a cognitive task. So, interpreting a radiology result, navigating the hospital hallways, and yes, of course, reading the medical literature, are all examples of cognitive tasks that AI can perform. It’s “artificial” because the machine is doing it. It’s “intelligence” because it’s a cognitive task.

Ok, so what’s Machine Learning then?

AI is a big field. Think of all of the cognitive tasks in healthcare you may want to automate: vision tasks (interpret radiology findings), navigation tasks (robots to deliver medicine), scheduling tasks (fit all nurses and doctors to a schedule given patient preferences and personnel constraints), and even interoperability tasks (gluing together EMR vocabularies). And of course, the most important task of all, Sudoku! (hint: use Constraint-Based Reasoning).

Now, some of these tasks require that the machine learn over time. This can mean someone teaches the machine (e.g., by giving it examples of good and bad PET scans). If you give examples, you are doing “supervised learning”. Or the machine can learn from making its own mistakes (this is called “reinforcement learning”). Or it may simply get better as it’s fed more and more data, such as learning which words in clinical notes mostly likely mean the same thing (if you don’t give examples, it is called “unsupervised learning.”).

But the big difference is that some AI tasks can be learned (e.g., improved over time) while others will not (for example, solving Sudoku uses rules and constraints, which can be setup ahead of time and never need to be updated again to solve the puzzles… but this is still a cognitive task!). For most healthcare purposes, the AI will involve machine learning because someone is trying to replicate (at least part of) a very complicated cognitive task, and it will be hard to enumerate all the rules and constraints ahead of time. But in the end, remember that machine learning is just a way to do artificial intelligence.

But then there is Deep Learning? Oye…

So, AI is replicating cognitive tasks, and one way to do that is to use machine learning. And within machine learning, there are various techniques, one of which is Deep Learning.

Some machine learning techniques make different decisions at different break points, like a flow chart (e.g., personalizing a particular therapy). Some machine learning techniques classify documents based on how likely the words in the documents associate to different categories (e.g., lab order vs. pathology report). Just like these, Deep Learning is a technique for machine learning. For this article, suffice it to say that Deep Learning is an approach where lots of interesting features that make the task solvable can be uncovered from lots and lots of data. For instance, when interpreting a scan there might be a little splotch of black and white that indicates a fracture, and Deep Learning can pick up on this nuance, without you having to tell it “little blotches mean fractures.”

So, Deep Learning is just one technique in the bag of machine learning tricks. And machine learning just means the system improves over time, in some way. And all of these are AI since they are replicating some cognitive task.

I hope the helps clear up some of the confusion about Deep Learning and AI, and which is which.